打牢地基-映射的底层实现(LinkedListMap、BSTMap)
打牢地基-映射的底层实现(LinkedListMap、BSTMap)
用户1081422
发布于 2020-04-08 10:41:08
发布于 2020-04-08 10:41:08
文章被收录于专栏：T客来了
章节
- 映射 Map
- 基于链表- LinkedList 的映射实现
- 基于二分搜索树- BST 的映射实现
- 链表、二分搜索树实现的映射复杂度分析
1. 映射-Map 的基本形态
1.Map 是一个顶级容器，map中存储的是键值对元素，key:value
2.Map 中的key 不能重复
2. 基于链表- LinkedList 的映射实现
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
# @Time : 2020/2/11 上午11:01
# @Author : bofengliu@tencent.com
# @Site :
# @File : LinkedListMap.py
# @Software: PyCharm
"""
class Node:
def __init__(self, key=None, val=None, next=None):
self.key = key
self.val = val
self.next = next
def to_string(self):
return self.key + ' : ' + self.val
class LinkedListMap:
def __init__(self):
self._dummy_head = Node(None, None, None)
self._size = 0
def add(self, key, val):
node = self._get_node(key)
if node is None:
self._dummy_head.next = Node(key, val, self._dummy_head.next)
self._size += 1
else:
node.val = val
def remove(self, key):
prev = self._dummy_head
while prev.next is not None:
if prev.next.key == key:
break
prev = prev.next
if prev.next is not None:
del_node = prev.next
prev.next = del_node.next
del_node.next = None
self._size -= 1
return del_node.val
return None
def contains(self, key):
return self._get_node(key) is not None
def get(self, key):
node = self._get_node(key)
if node is not None:
return node.val
return None
def set(self, key, new_val):
node = self._get_node(key)
if node is None:
raise (Exception, key + " doesn't exist!")
node.val = new_val
def get_size(self):
return self._size
def is_empty(self):
return self._size == 0
def _get_node(self, key):
cur = self._dummy_head.next
while cur is not None:
if cur.key == key:
return cur
cur = cur.next
return None
注意:设置了 getnode 辅助函数, contains()、get()、set() 都会用的到
3. 基于二分搜索树- BST 的映射实现
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
# @Time : 2020/2/11 上午11:01
# @Author : bofengliu@tencent.com
# @Site :
# @File : BSTMap.py
# @Software: PyCharm
"""
class Node:
def __init__(self, key=None, val=None, left=None, right=None):
self.key = key
self.val = val
self.left = left
self.right = right
class BSTMap:
def __init__(self, root: Node = None):
self._root = root
self._size = 0
def get_size(self):
return self._size
def is_empty(self):
return self._size == 0
def contains(self, key):
return self._get_node(self._root, key) is not None
def _get_node(self, node, key):
"""
递归获取 key 对应的 node
:param node:
:param key:
:return:
"""
if node is None:
return None
if node.key == key:
return node
if key < node.key:
return self._get_node(node.left, key)
if key > node.key:
return self._get_node(node.right, key)
def add(self, key, val):
self._root = self._add_element(self._root, key, val)
def _add_element(self, root, key, val):
"""
递归构建二分搜索树
:param root:
:param e:
:return:
"""
if root is None:
self._size += 1
"""
返回的是子树根节点
"""
return Node(key, val)
elif key < root.key:
root.left = self._add_element(root.left, key, val)
elif key > root.key:
# insert
root.right = self._add_element(root.right, key, val)
else:
# or update
root.val = val
return root
def get(self, key):
node = self._get_node(self._root, key)
if node is None:
return None
return node.val
def set(self, key, new_val):
node = self._get_node(self._root, key)
if node is None:
raise (Exception, key + " doesn't exist!")
node.val = new_val
def _minimum(self, node: Node):
"""
递归-找到二分搜索树中最小元素
:param node:
:return:
"""
if node.left is None:
return node
return self._minimum(node.left)
def _remove_minimum(self, node: Node):
"""
删除二分搜索树中的最小节点，返回删除节点后新的二分搜索树的根
:param node:
:return:
"""
if node.left is None:
# 此刻已经遍历到底，当前node为已经遍历到的值
right_node = node.right
node.right = None
self._size -= 1
return right_node
node.left = self._remove_minimum(node.left)
return node
def remove(self, key):
"""
从bst 中删除元素为key的节点
:param key:
:return:
"""
node = self._get_node(self._root, key)
if node is not None:
self._root = self._remove(self._root, key)
return node.val
return None
def _remove(self, node: Node, key):
"""
删除以node为根的BST 中节点为e的节点，递归算法
返回删除节点后新的二分搜索树中的根
:param node:
:param key:
:return:
"""
if node is None:
return None
if key < node.key:
node.left = self._remove(node.left, key)
return node
elif key > node.key:
node.right = self._remove(node.right, key)
return node
else: # key == node.key
if node.left is None:
right_node = node.right
node.right = None
self._size -= 1
return right_node
if node.right is None:
left_node = node.left
node.left = None
self._size -= 1
return left_node
del_node = node
# 找到后继节点,当前被删除的右子树的最小元素
next_node = self._minimum(del_node.right)
next_node.right = self._remove_minimum(del_node.right)
next_node.left = del_node.left
del_node.left = del_node.right = None
return next_node
注意:与之前的BST 不同，BST 的数据域e 变成了 key，val;
4. 链表、二分搜索树实现的映射复杂度分析
时间复杂度:
映射的时间复杂度.png注意:二分搜索树在最差情况下会退化成链表，解决这个问题需要用AVL树
有序映射 & 无序映射
有序映射即键是具有顺序性的->基于搜索树实现的 ，无序映射中的键是没有顺序的
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原始发表：2020-02-12，如有侵权请联系 cloudcommunity@tencent.com 删除
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